Estimation with Inequality Constraints on Parameters and Truncation of the Sampling Distribution.

Theoretical constraints on economic model parameters often are in the form of inequality restrictions. For example, many theoretical results are in the form of monotonicity or nonnegativity restrictions. Inequality constraints can truncate sampling distributions of parameter estimators, so that asymptotic normality no longer is possible. Sampling theoretic asymptotic inference is thereby greatly complicated or compromised. We use numerical methods to investigate the resulting sampling properties of inequality-constrained estimators produced by popular methods of imposing inequality constraints, with particular emphasis on the method of squaring, which is the most widely used method in the applied literature on estimating integrable neoclassical systems of demand equations. See Barnett and Binner (2004).Asymptotics, truncated sampling distribution, nonidentified sign, inequality constraints, bootstrap, jackknife.

Rotterdam vs Almost Ideal Models: Will the Best Demand Specification Please Stand Up?

Among the many demand specifications in the literature, the Rotterdam model and the Almost Ideal Demand System (AIDS) have particularly long histories, have been highly developed, and are often applied in consumer demand systems modeling. Using Monte Carlo techniques, we seek to determine which model performs better in terms of its ability to recover the true elasticities of demand. We derive the correct formulae for the AIDS models elasticities, when the Törnqvist or two modified versions of the Stone index are used to linearize the model. The resulting linearized AIDS are compared to the full AIDS

A note on nonidentification in truncated sampling distribution estimation

Theoretical constraints on economic model parameters often are in the form of inequality restrictions. For example, many theoretical results are in the form of monotonicity or nonnegativity restrictions. Inequality constraints can truncate sampling distributions of parameter estimators, so that asymptotic normality no longer is possible. Sampling theoretic asymptotic inference is thereby greatly complicated or compromised. In Barnett and Seck (2009), which will be appear in volume 1 number 1 of the new journal, Journal of Statistics: Advances in Theory and Applications, we use numerical methods to investigate the resulting sampling properties of estimation with inequality constraints, with particular emphasis on the method of squaring, which is the most widely used method in applied literature on estimating integrable neoclassical systems of equations. In this note, we make our most important results more widely and easily available.Inequality constraints, truncated sampling distribution, nonidentification, method of squaring, numerical methods, small sample properties, asymptotic properties

Rotterdam vs Almost Ideal Models: Will the Best Demand Specification Please Stand Up?

Among the many demand specifications in the literature, the Rotterdam model and the Almost Ideal Demand System (AIDS) have particularly long histories, have been highly developed, and are often applied in consumer demand systems modeling. Using Monte Carlo techniques, we seek to determine which model performs better in terms of its ability to recover the true elasticities of demand. We derive the correct formulae for the AIDS models elasticities, when the Törnqvist or two modified versions of the Stone index are used to linearize the model. The resulting linearized AIDS are compared to the full AIDS.Rotterdam Model; Almost Ideal Model; consumer demand system; Monte Carlo study; flexible functional forms

A note on nonidentification in truncated sampling distribution estimation

This is the publisher's version, also available electronically from http://www.economicsbulletin.com.Theoretical constraints on economic model parameters often are in the form of inequality restrictions. For example, many theoretical results are in the form of monotonicity or nonnegativity restrictions. Inequality constraints can truncate sampling distributions of parameter estimators, so that asymptotic normality no longer is possible. Sampling theoretic asymptotic inference is thereby greatly complicated or compromised. In Barnett and Seck (2009), which will be appear in volume 1 number 1 of the new journal, Journal of Statistics: Advances in Theory and Applications, we use numerical methods to investigate the resulting sampling properties of estimation with inequality constraints, with particular emphasis on the method of squaring, which is the most widely used method in applied literature on estimating integrable neoclassical systems of equations. In this note, we make our most important results more widely and easily available

Analyzing the Mobile-Banking Adoption Process among Low-Income Populations: A Sequential Logit Model

International audienceThe purpose of this study is to uncover the socioeconomic factors that explain the adoption of mobile banking (mbanking),based on data collected from households in the suburbs of Dakar (Senegal). Starting from the hypothesisthat adopting an innovation goes through three stages, at each stage we identify the factors that explain adoption. Inthe first stage, that of "knowledge", the individual must know about the product and its uses. In the second stage, thatof "possession", the person must test the product. If the product is accessible and its advantages are observable,he/she can finally adopt it in the last stage of the process. Therefore, the steps "knowledge" and "possession" arerequired passages in the adoption process. In this article, we use a sequential logit model to highlight the determinantsat each level of this process. The results show that age was the only determining factor in the first stage of adoption,that is, "knowledge" of m-banking. In the second phase, other factors appeared in addition; cognitive factors came intoplay, such as literacy, education level, as well as financial factors such as membership in a ROSCA (rotating credit andsavings scheme) that influenced the ‘possession' of m-banking. At the final stage of the adoption process, the variableseducation level, wages and owning a business were the factors involved in the adoption of m-banking

Estimation with Inequality Constraints on Parameters and Truncation of the Sampling Distribution

Theoretical constraints on economic model parameters often are in the form of inequality restrictions. For example, many theoretical results are in the form of monotonicity or nonnegativity restrictions. Inequality constraints can truncate sampling distributions of parameter estimators, so that asymptotic normality no longer is possible. Sampling theoretic asymptotic inference is thereby greatly complicated or compromised. We use numerical methods to investigate the resulting sampling properties of inequality-constrained estimators produced by popular methods of imposing inequality constraints, with particular emphasis on the method of squaring, which is the most widely used method in the applied literature on estimating integrable neoclassical systems of demand equations. See Barnett and Binner (2004)

Estimation with inequality constraints on the parameters: dealing with truncation of the sampling distribution.

Theoretical constraints on economic-model parameters often are in the form of inequality restrictions. For example, many theoretical results are in the form of monotonicity or nonnegativity restrictions. Inequality constraints can truncate sampling distributions of parameter estimators, so that asymptotic normality no longer is possible. Sampling theoretic asymptotic inference is thereby greatly complicated or compromised. We use numerical methods to investigate the resulting sampling properties of inequality constrained estimators produced by popular methods of imposing inequality constraints. In particular, we investigate the possible bias in the asymptotic standard errors of estimators of inequality constrained estimators, when the constraint is imposed by the popular method of squaring. That approach is known to violate a regularity condition in the available asymptotic proofs regarding the unconstrained estimator, since the sign of the unconstrained estimator, prior to squaring, is nonidentified

Estimation with inequality constraints on the parameters: dealing with truncation of the sampling distribution.

Theoretical constraints on economic-model parameters often are in the form of inequality restrictions. For example, many theoretical results are in the form of monotonicity or nonnegativity restrictions. Inequality constraints can truncate sampling distributions of parameter estimators, so that asymptotic normality no longer is possible. Sampling theoretic asymptotic inference is thereby greatly complicated or compromised. We use numerical methods to investigate the resulting sampling properties of inequality constrained estimators produced by popular methods of imposing inequality constraints. In particular, we investigate the possible bias in the asymptotic standard errors of estimators of inequality constrained estimators, when the constraint is imposed by the popular method of squaring. That approach is known to violate a regularity condition in the available asymptotic proofs regarding the unconstrained estimator, since the sign of the unconstrained estimator, prior to squaring, is nonidentified

Estimation with Inequality Constraints on Parameters and Truncation of the Sampling Distribution

Theoretical constraints on economic model parameters often are in the form of inequality restrictions. For example, many theoretical results are in the form of monotonicity or nonnegativity restrictions. Inequality constraints can truncate sampling distributions of parameter estimators, so that asymptotic normality no longer is possible. Sampling theoretic asymptotic inference is thereby greatly complicated or compromised. We use numerical methods to investigate the resulting sampling properties of inequality-constrained estimators produced by popular methods of imposing inequality constraints, with particular emphasis on the method of squaring, which is the most widely used method in the applied literature on estimating integrable neoclassical systems of demand equations. See Barnett and Binner (2004)